AI Model NeoPred Predicts Lung Cancer Treatment Response Before Surgery Using Dual-Phase CT Imaging

Researchers led by Professor Jianxing He at the First Affiliated Hospital of Guangzhou Medical University have developed NeoPred, an innovative artificial intelligence model that predicts major pathological response (MPR) to neoadjuvant chemo-immunotherapy in non-small cell lung cancer (NSCLC (search)Search disease) patients before surgical intervention. The breakthrough, published in the Journal for ImmunoTherapy of Cancer in May 2025, addresses a critical gap in lung cancer treatment by enabling real-time, non-invasive predictions of treatment efficacy.

Addressing Clinical Need for Preoperative Response Assessment

Neoadjuvant chemo-immunotherapy has emerged as a recommended treatment for resectable or locally advanced NSCLC (search)Search disease, increasing tumor resectability and improving overall prognosis. However, the evaluation of treatment efficacy conventionally relies on postoperative pathological assessments, which can only be performed after tumor resection. This delay limits clinicians' ability to dynamically refine therapeutic strategies during the neoadjuvant window and extends patient exposure to potentially ineffective regimens.

NeoPred targets this clinical impasse by utilizing dual-phase computed tomography (CT) imaging—capturing tumor morphology and response dynamics both before the initiation of therapy and just prior to surgery. This dual temporal perspective allows the AI model to quantify subtle morphological changes induced by chemo-immunotherapy that often escape human visual assessment.

Multimodal AI Architecture and Study Design

The model incorporates a multimodal framework by integrating critical clinical parameters such as age, sex, body mass index, and tumor staging alongside imaging data. These data streams are fused within advanced 3D convolutional neural networks, forming a robust predictive architecture that tailors the evaluation to each patient's complex clinical profile.

The study underlying NeoPred drew on a diverse cohort of 509 NSCLC (search)Search disease patients across four distinguished thoracic oncology centers. The research team adopted a rigorous methodology involving retrospective model training and validation on 459 cases, supplemented by prospective real-world testing on 50 additional patients. To further validate their findings externally, 59 independent cases from collaborating institutions served as a test set, reinforcing the model's generalizability and clinical applicability.

Superior Performance Metrics and Clinical Validation

NeoPred demonstrated formidable performance metrics throughout its evaluations. In the external validation cohort, the AI system achieved an area under the receiver operating characteristic curve (AUC) of 0.772 using imaging data alone; this precision improved with the inclusion of clinical variables, elevating the AUC to 0.787. The model's predictive power shone in prospective clinical application, where NeoPred surpassed expert thoracic surgeons' interpretation accuracy, with an AUC of 0.760 compared to the human benchmark of 0.720.

When nine expert thoracic surgeons were asked to interpret CT scans initially without and subsequently with access to NeoPred's predictive heatmaps, their diagnostic accuracy surged to 82%, with an impressive AUC elevation to 0.829. This synergy exemplifies how AI tools can augment clinical expertise rather than supplant it, cultivating a collaborative interface that leverages machine precision and human judgment to optimize outcomes.

Breakthrough in Detecting "Pseudo-Stable" Responders

One of the most compelling breakthroughs emerged from the study's investigation into "stable disease" (SD) cases classified according to the RECIST criteria. While these patients exhibit limited apparent tumor shrinkage post-therapy, NeoPred successfully uncovered underlying major pathological responders within this group, achieving AUCs of 0.742 in external datasets and an even more striking 0.833 in prospective cases. This capability to detect "pseudo-stable" responders highlights the model's sensitivity to nuanced morphological and dynamic tumor transformations that traditional radiological assessments might overlook.

Clinical Impact and Treatment Optimization

NeoPred's clinical implications extend beyond mere prognostication. By delivering early and reliable predictions of pathological response one to two weeks before surgery, the model facilitates evidence-based adjustments in perioperative strategies, potentially sparing patients from unnecessary surgical morbidity or enabling timely alterations in systemic therapy. The integration of AI-generated quantitative metrics into multidisciplinary team discussions promises to streamline workflows, promote objective risk stratification, and enhance collaborative decision-making.

Part of Comprehensive AI Ecosystem

This breakthrough AI initiative forms an integral component of a broader ecosystem of computational tools masterminded by Prof. He's team, designed to address the multifaceted challenges of lung cancer management. Technologies span early detection platforms combining cell-free DNA methylation assays and low-dose CT scans, advanced proteomics for plasma biomarker identification, natural language processing algorithms for electronic health record mining, and sophisticated deep learning models predicting gene mutations directly from histopathology slides.

The integration of federated learning platforms within the ecosystem, such as the CAIMEN system fostered across over 40 institutions, underscores the team's commitment to data security and collaborative model refinement without compromising patient privacy. This decentralized approach to AI training not only expands the diversity of representative data but also propels diagnostic accuracy across geographic and institutional boundaries.

As lung cancer remains the leading cause of cancer mortality worldwide, innovations like NeoPred usher in a new era where AI-driven insights guide therapeutic timelines, refine surgical candidacy, and personalize treatment trajectories with unprecedented granularity and timeliness. The convergence of multimodal imaging, deep learning, and clinical data modeling exemplified by NeoPred reflects the burgeoning potential of AI to revolutionize oncologic care, bringing hope for improved survival and quality of life to patients confronting NSCLC (search)Search disease.